Feasibility of Detecting Different Genotypes of Mentha plant by E-nose Technique

Articles in Press

Document Type : Research Article-en

Authors

• H. Zaki Dizaji ¹
• M. Mahmoodi Surestani ²
• N. Aghili Nategh ³
• A. Boveiri Dehsheikh ²

¹ Department of Biosystems Engineering, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran

² Department of Horticulture, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran

³ Department of Agricultural Machinery Engineering, Sonqor Agriculture Faculty, Razi University, Kermanshah, Iran

https://doi.org/10.22067/jam.2025.92417.1354

Abstract

In botanical terms, the classification of plants reveals a multitude of species derived from different sources. The first step for quality control of herbal medicines is to identify their different species and genotypes. The present study investigated the classification of ten different mint genotypes using Gas Chromatography-mass Spectrometry (GC-MS) and an electronic nose (e-nose) system utilizing Metal Oxide Semiconductor (MOS) sensors. Leaf samples were harvested from various mint genotypes, and subsequently, the system sensors' responses to each of these samples were recorded. The classification of plants was performed using biplot diagrams based on GC and GC-MS data, with clustering facilitated by the Ward method. The responses of all e-nose sensors were further analysed through various approaches, including Principal Components Analysis (PCA), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), and Artificial Neural Network (ANN). The results from the qualitative analysis of essential oils via GC-MS demonstrate that more than 99% of the identified compounds belong to four chemical groups: hydrocarbon and oxygenated monoterpenes, as well as hydrocarbon and oxygenated sesquiterpenes. Also, based on biplot analysis, different mint populations could be generally divided into 8 groups. The results of principal component analysis showed that the first two main components can cover a total of 97% of the data variance. The classification accuracy achieved through e-nose data for LDA, QDA, and ANN was 98.9%, 99.9%, and 96%, respectively. Proper classification of mint genotypes by e-nose system could be used as a sensitive, reliable, and low-cost alternative to traditional methods.

Keywords

• Artificial Intelligent methods
• Classification
• E-nose
• Essential oil

Main Subjects

• Post-harvest technologies

©2025 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0)

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